This invention relates to adaptive beamforming, image forming and image processing for ultrasound imaging. In particular, the adaptation is a function of coherence of echo received on a plurality of transducer elements.
In ultrasound imaging systems, signals from targets that are on-axis or in the main lobe sum with signals from targets that are off-axis, in the grating-lobe or side-lobes, reducing channel-to-channel coherence of signals. Reduced coherence may result in reduced detail resolution, contrast resolution, dynamic range and signal-to-noise ratio. Focusing errors from sound velocity and/or attenuation inhomogeneities elevates sensitivity to off-axis targets. These focusing errors further reduce the channel-to-channel coherence.
A measure of an amount of coherence has been used to adapt processing. The ratio of the amplitude of coherently summed signals to incoherently summed signals, i.e., coherence factor, is computed for each pixel. Beamforming delay is applied to the signals before calculating the coherence factor. The coherence factor or a combination of the coherence factor and the amplitude is displayed. In another approach, the coherence factor is calculated as the amplitude ratio of conventionally focused to unfocused (e.g., flat time delay profile) signals. In both approaches, if the coherence factor is low then acoustic clutter is assumed high, and therefore the pixel brightness is suppressed.
In U.S. Pat. No. 6,432,054, a coherence factor is used as a weight. Two received beams are summed incoherently (i.e., compounded) and summed coherently (i.e., synthesized). The coherence factor from one or both of the two received beams weights the compounded and synthesized beams. The weighted beams are then used to form an image. If the coherence factor is high, the weight for the synthesized beam is increased and thus the resolution is improved. Otherwise, the weight for the compounded beam is increased and thus contrast resolution is improved.
Coherence factor adaptive pixel weighting techniques may cause increased speckle variance. Low-pass filtering coherence factor images prior to determining the pixel weights may reduce the effect on the speckle variance. But the filtered coherence factor approach may not perform well in regions outside the depth of field of transmit focus.
In a different approach for adaptive side-lobe suppression, data from parallel receive beams in response to and around a fixed transmit beam (i.e., single transmit imaging) are used to estimate side-lobe contribution. For every pixel, a total least-squares calculation is performed. The contribution of side-lobes is then suppressed. However, the calculations require a large memory and an iterative scheme, making real-time implementation difficult and expensive. An approximation to parallel receive beamforming has been provided with a Fourier transform across channels of the received signal.
A generalized coherence factor including non-DC components of the Discrete Fourier Transform across received channels has been proposed. This generalized coherence factor provides an index of the beamforming quality that may perform adequately even for speckle targets and outside the depth of field. The generalized coherence factor weights image data to reduce the image brightness where the coherence factor is low, i.e., focusing is poor. An efficient FFT based technique has been proposed to calculate the generalized coherence factor.